{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e68502a2",
   "metadata": {},
   "source": [
    "# 0. Import Libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b29e0256",
   "metadata": {},
   "source": [
    "!pip install pandas\n",
    "!pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "86f34158",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow.keras as keras\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.layers import *\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.io import loadmat\n",
    "from scipy.io import savemat\n",
    "import math\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "from matplotlib.ticker import LinearLocator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c8fc4521",
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy.random import seed\n",
    "seed(0)\n",
    "tf.random.set_seed(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a011e9b",
   "metadata": {},
   "source": [
    "# 1. Define Something in Advance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f7b98628",
   "metadata": {},
   "outputs": [],
   "source": [
    "def U(x):\n",
    "    return np.exp(-x)*np.sin(m*np.pi*x**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52694299",
   "metadata": {},
   "source": [
    "# 2. Initialize the Grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cb09ad87",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_domain = np.linspace(0, np.pi, 200)\n",
    "delta = x_domain[1] - x_domain[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aff173b7",
   "metadata": {},
   "source": [
    "# 3. Define Physical Info."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45a38f16",
   "metadata": {},
   "source": [
    "    suppose: x = x[0]; u_x = x[1]; dudx = x[2]; du2dx = x[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9f286f45",
   "metadata": {},
   "outputs": [],
   "source": [
    "governing_equation_components = []\n",
    "governing_equation_components.append(Lambda(lambda x: l*x[2]))\n",
    "governing_equation_components.append(Lambda(lambda x: g*tf.sin(x[2])))\n",
    "governing_equation_components.append(Lambda(lambda x: x[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8ed72081",
   "metadata": {},
   "outputs": [],
   "source": [
    "governing_equation_mask = x_domain*0 + 1\n",
    "governing_equation_mask[0] = 0\n",
    "governing_equation_mask[-1] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bed8ff59",
   "metadata": {},
   "outputs": [],
   "source": [
    "l = 1\n",
    "g = 1\n",
    "m = 3\n",
    "fx = governing_equation_mask*(\n",
    "           l*(2*m*np.pi*x_domain*np.exp(-x_domain)*np.cos(m*np.pi*x_domain**2)-np.exp(-x_domain)*np.sin(m*np.pi*x_domain**2)) + \n",
    "           g*np.sin(2*m*np.pi*x_domain*np.exp(-x_domain)*np.cos(m*np.pi*x_domain**2)-np.exp(-x_domain)*np.sin(m*np.pi*x_domain**2)) + \n",
    "           np.exp(-x_domain)*np.sin(m*np.pi*x_domain**2)    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9e65ebff",
   "metadata": {},
   "outputs": [],
   "source": [
    "estimate_equation_form = False\n",
    "equation_component_combination = [1.0,1.0,1.0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a647c8f2",
   "metadata": {},
   "source": [
    "# 4. Define the Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee89c021",
   "metadata": {},
   "source": [
    "    suppose: x = x[0]; u_x = x[1]; dudx = x[2]; du2dx = x[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "28d1ac32",
   "metadata": {},
   "outputs": [],
   "source": [
    "observation_components = []\n",
    "observation_components.append(Lambda(lambda x: x[1]))\n",
    "observation_components.append(Lambda(lambda x: x[2]))\n",
    "observation_components.append(Lambda(lambda x: x[3]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41dc785c",
   "metadata": {},
   "source": [
    "    format: [x,combination,value]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5b0cc96e",
   "metadata": {},
   "outputs": [],
   "source": [
    "observation_data = []\n",
    "observation_data.append([0,[1,0,0],U(0)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2270ab5c",
   "metadata": {},
   "source": [
    "# 5. Define PICN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "50428a60",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient_kernal_init(shape, dtype=tf.float32):\n",
    "    return tf.constant([[[-1.0/(delta)]],[[1.0/(delta)]]], dtype=dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2d99b8e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient_2_kernal_init(shape, dtype=tf.float32):\n",
    "    return tf.constant([[[1.0/(delta**2)]],[[-2.0/(delta**2)]],[[1.0/(delta**2)]]], dtype=dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "858a79b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = [keras.layers.Input(shape=(1,1)),keras.layers.Input(shape=(len(x_domain),1))]\n",
    "\n",
    "hidden_field = keras.layers.Conv1DTranspose(filters=1, \n",
    "                                            kernel_size=len(x_domain)+4, \n",
    "                                            activation='linear')(inputs[0])\n",
    "coordinates = inputs[1]\n",
    "field = keras.layers.Conv1D(filters=1, \n",
    "                            kernel_size=3, \n",
    "                            padding='valid', \n",
    "                            activation='linear')(hidden_field)\n",
    "gradient_field = keras.layers.Conv1D(filters=1, \n",
    "                                     kernel_size=2, \n",
    "                                     padding='valid',\n",
    "                                     use_bias=False,\n",
    "                                     trainable=False,\n",
    "                                     kernel_initializer=gradient_kernal_init)(field)\n",
    "gradient_2_field = keras.layers.Conv1D(filters=1, \n",
    "                                       kernel_size=3, \n",
    "                                       padding='valid',\n",
    "                                       use_bias=False,\n",
    "                                       trainable=False,\n",
    "                                       kernel_initializer=gradient_2_kernal_init)(field)\n",
    "phycial_fields = [coordinates,field[:,1:-1,:],gradient_field[:,1:,:],gradient_2_field]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f9985fe3",
   "metadata": {},
   "outputs": [],
   "source": [
    "if estimate_equation_form==True:\n",
    "    tf_governing_equation_components = [component(phycial_fields) for component in governing_equation_components]\n",
    "    concat_equation_components = Lambda(lambda x: tf.concat(x,axis=-1))(tf_governing_equation_components)\n",
    "    governing_equation = keras.layers.Conv1D(filters=1, \n",
    "                                             kernel_size=1, \n",
    "                                             padding='valid',\n",
    "                                             use_bias=False)(concat_equation_components)\n",
    "else:\n",
    "    tf_weighted_governing_equation_components = [weight*component(phycial_fields) for [weight,component] in zip(equation_component_combination,governing_equation_components)]\n",
    "    concat_weighted_equation_components = Lambda(lambda x: tf.concat(x,axis=-1))(tf_weighted_governing_equation_components)\n",
    "    governing_equation = Lambda(lambda x: tf.reduce_sum(x,axis=-1,keepdims=True))(concat_weighted_equation_components)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "79aaf2f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "tf_observation_components = [component(phycial_fields) for component in observation_components]\n",
    "observation_list = []\n",
    "for data in observation_data:\n",
    "    if data[0] <= x_domain[0]:\n",
    "        left_x_position_index = 0\n",
    "        right_x_position_index = 1\n",
    "        left_x_position_weight = 1\n",
    "        right_x_position_weight = 0\n",
    "    elif data[0] >= x_domain[-1]:\n",
    "        left_x_position_index = -2\n",
    "        right_x_position_index = -1\n",
    "        left_x_position_weight = 0\n",
    "        right_x_position_weight = 1\n",
    "    else:\n",
    "        left_x_position_index = int(np.round((data[0] - x_domain[0])/delta))\n",
    "        right_x_position_index = int(np.round(left_x_position_index + 1))\n",
    "        left_x_position_weight = 1-(data[0] - (x_domain[0]+delta*left_x_position_index))/delta\n",
    "        right_x_position_weight = 1-left_x_position_weight\n",
    "        \n",
    "    left_position_observation_components = [component[:,left_x_position_index,:] for component in tf_observation_components]\n",
    "    right_position_observation_components = [component[:,right_x_position_index,:] for component in tf_observation_components]\n",
    "    \n",
    "    concat_weighted_left_position_observation_components = Lambda(lambda x: tf.concat(x,axis=-1))([weight*component for [weight,component] in zip(data[1],left_position_observation_components)])\n",
    "    concat_weighted_right_position_observation_components = Lambda(lambda x: tf.concat(x,axis=-1))([weight*component for [weight,component] in zip(data[1],right_position_observation_components)])\n",
    "    \n",
    "    left_observation = Lambda(lambda x: tf.reduce_sum(x,axis=-1,keepdims=True))(concat_weighted_left_position_observation_components)\n",
    "    right_observation = Lambda(lambda x: tf.reduce_sum(x,axis=-1,keepdims=True))(concat_weighted_right_position_observation_components)\n",
    "    \n",
    "    observation_list.append(left_x_position_weight*left_observation + right_x_position_weight*right_observation)\n",
    "    \n",
    "observations = Lambda(lambda x: tf.concat(x,axis=-1))(observation_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c2c9be15",
   "metadata": {},
   "outputs": [],
   "source": [
    "pde_model = keras.Model(inputs=inputs, outputs=phycial_fields)\n",
    "pde_model_train = keras.Model(inputs=inputs, outputs=[governing_equation,observations])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2ea9c324",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Tensor 'input_1:0' shape=(None, 1, 1) dtype=float32>,\n",
       " <tf.Tensor 'input_2:0' shape=(None, 200, 1) dtype=float32>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pde_model_train.input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e102d5b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Tensor 'lambda_7/Sum:0' shape=(None, 200, 1) dtype=float32>,\n",
       " <tf.Tensor 'lambda_12/concat/concat:0' shape=(None, 1) dtype=float32>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pde_model_train.output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dbf6c1e",
   "metadata": {},
   "source": [
    "# 6. Prepare the Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e13964ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1, 1)\n"
     ]
    }
   ],
   "source": [
    "unit_constant = np.asarray([[[1.0]]],dtype=np.float32)\n",
    "training_input_data_0 = np.repeat(unit_constant, 1, axis=0)\n",
    "print(training_input_data_0.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "03abae6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 200, 1)\n"
     ]
    }
   ],
   "source": [
    "training_input_data_1 = np.expand_dims(x_domain.astype(np.float32),axis=[0,-1])\n",
    "print(training_input_data_1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "83251413",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 200, 1)\n"
     ]
    }
   ],
   "source": [
    "training_label_data_0 = np.expand_dims(fx,axis=(0,-1))\n",
    "print(training_label_data_0.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "09992122",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1)\n"
     ]
    }
   ],
   "source": [
    "training_label_data_1 = np.expand_dims(np.asarray([data[2] for data in observation_data]),axis=[0])\n",
    "print(training_label_data_1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3ae901b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_input_data = [training_input_data_0,training_input_data_1]\n",
    "training_label_data = [training_label_data_0,training_label_data_1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d3ecd9a",
   "metadata": {},
   "source": [
    "# 6. Train the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "da87972b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['loss', 'lambda_7_loss', 'lambda_12_loss']\n"
     ]
    }
   ],
   "source": [
    "pde_model_train.compile(optimizer=keras.optimizers.Adam(), loss=\"mse\")\n",
    "pde_model_train.save_weights('picn_initial_weights.h5')\n",
    "temp_history = pde_model_train.fit(x=training_input_data, y=training_label_data, epochs=1, verbose=0)\n",
    "history_keys = []\n",
    "for key in temp_history.history.keys():\n",
    "    history_keys.append(key)\n",
    "print(history_keys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "daa7785a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def record_predictions():\n",
    "    [_,ux, dudx, d2udx2] = pde_model.predict(training_input_data)\n",
    "    ux_list.append(ux[0,:,0])\n",
    "    dudx_list.append(dudx[0,:,0])\n",
    "    d2udx2_list.append(d2udx2[0,:,0])\n",
    "\n",
    "class Per_X_Epoch_Record(tf.keras.callbacks.Callback):\n",
    "    def __init__(self, record_interval, verbose=1):\n",
    "        super(tf.keras.callbacks.Callback, self).__init__()\n",
    "        self.total_loss = history_keys[0]\n",
    "        self.domain_loss = history_keys[1]\n",
    "        self.bdc_loss = history_keys[2]\n",
    "        self.previous_total_loss = 9999999\n",
    "        self.record_interval = record_interval;\n",
    "        self.verbose = verbose\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs={}):\n",
    "        \n",
    "        if epoch%self.record_interval == 0:\n",
    "            \n",
    "            current_total_loss = logs.get(self.total_loss)\n",
    "            current_domain_loss = logs.get(self.domain_loss)\n",
    "            current_bdc_loss = logs.get(self.bdc_loss)\n",
    "            \n",
    "            epoch_number_list.append(epoch)\n",
    "            total_loss_list.append(current_total_loss)\n",
    "            domain_loss_list.append(current_domain_loss)\n",
    "            boundary_loss_list.append(current_bdc_loss)\n",
    "        \n",
    "            if current_total_loss < self.previous_total_loss:\n",
    "                self.previous_total_loss = current_total_loss\n",
    "                pde_model_train.save_weights('picn_best_weights.h5')\n",
    "            \n",
    "            if self.verbose > 0:\n",
    "                print(\"epoch: {:10.5f} | total_loss: {:10.5f} | domain_loss: {:10.5f} | bdc_loss: {:10.5f}\".format(epoch,current_total_loss,current_domain_loss,current_bdc_loss))\n",
    "        \n",
    "            # evaluate the errors in f-domain\n",
    "            record_predictions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2dfa38e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "callbacks = [\n",
    "    Per_X_Epoch_Record(record_interval=200,verbose=1),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "09eccbbe",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:    0.00000 | total_loss:    6.28446 | domain_loss:   62.84462 | bdc_loss:    0.00000\n",
      "epoch:  200.00000 | total_loss:    1.46849 | domain_loss:   14.68311 | bdc_loss:    0.00020\n",
      "epoch:  400.00000 | total_loss:    1.30591 | domain_loss:   13.05690 | bdc_loss:    0.00024\n",
      "epoch:  600.00000 | total_loss:    1.24993 | domain_loss:   12.49792 | bdc_loss:    0.00016\n",
      "epoch:  800.00000 | total_loss:    1.21686 | domain_loss:   12.16703 | bdc_loss:    0.00017\n",
      "epoch: 1000.00000 | total_loss:    1.18510 | domain_loss:   11.84955 | bdc_loss:    0.00017\n",
      "epoch: 1200.00000 | total_loss:    1.15221 | domain_loss:   11.51982 | bdc_loss:    0.00026\n",
      "epoch: 1400.00000 | total_loss:    1.10295 | domain_loss:   11.02722 | bdc_loss:    0.00025\n",
      "epoch: 1600.00000 | total_loss:    1.03821 | domain_loss:   10.38136 | bdc_loss:    0.00009\n",
      "epoch: 1800.00000 | total_loss:    0.94707 | domain_loss:    9.46921 | bdc_loss:    0.00017\n",
      "epoch: 2000.00000 | total_loss:    0.84719 | domain_loss:    8.47055 | bdc_loss:    0.00014\n",
      "epoch: 2200.00000 | total_loss:    0.50273 | domain_loss:    5.02719 | bdc_loss:    0.00002\n",
      "epoch: 2400.00000 | total_loss:    0.42224 | domain_loss:    4.22169 | bdc_loss:    0.00008\n",
      "epoch: 2600.00000 | total_loss:    0.32571 | domain_loss:    3.25709 | bdc_loss:    0.00000\n",
      "epoch: 2800.00000 | total_loss:    0.23233 | domain_loss:    2.32326 | bdc_loss:    0.00000\n",
      "epoch: 3000.00000 | total_loss:    0.17058 | domain_loss:    1.70562 | bdc_loss:    0.00002\n",
      "epoch: 3200.00000 | total_loss:    0.16936 | domain_loss:    1.69340 | bdc_loss:    0.00002\n",
      "epoch: 3400.00000 | total_loss:    0.09508 | domain_loss:    0.95080 | bdc_loss:    0.00000\n",
      "epoch: 3600.00000 | total_loss:    0.09213 | domain_loss:    0.92127 | bdc_loss:    0.00000\n",
      "epoch: 3800.00000 | total_loss:    0.09130 | domain_loss:    0.91301 | bdc_loss:    0.00000\n",
      "epoch: 4000.00000 | total_loss:    0.09061 | domain_loss:    0.90608 | bdc_loss:    0.00000\n",
      "epoch: 4200.00000 | total_loss:    0.08988 | domain_loss:    0.89878 | bdc_loss:    0.00000\n",
      "epoch: 4400.00000 | total_loss:    0.08934 | domain_loss:    0.89341 | bdc_loss:    0.00000\n",
      "epoch: 4600.00000 | total_loss:    0.08605 | domain_loss:    0.86046 | bdc_loss:    0.00000\n",
      "epoch: 4800.00000 | total_loss:    0.04499 | domain_loss:    0.44989 | bdc_loss:    0.00000\n",
      "epoch: 5000.00000 | total_loss:    0.04342 | domain_loss:    0.43418 | bdc_loss:    0.00000\n",
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      "epoch: 9600.00000 | total_loss:    0.00045 | domain_loss:    0.00451 | bdc_loss:    0.00000\n",
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      "epoch: 10000.00000 | total_loss:    0.00035 | domain_loss:    0.00354 | bdc_loss:    0.00000\n",
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      "epoch: 11600.00000 | total_loss:    0.00023 | domain_loss:    0.00225 | bdc_loss:    0.00000\n",
      "epoch: 11800.00000 | total_loss:    0.00020 | domain_loss:    0.00199 | bdc_loss:    0.00000\n",
      "epoch: 12000.00000 | total_loss:    0.00020 | domain_loss:    0.00202 | bdc_loss:    0.00000\n",
      "epoch: 12200.00000 | total_loss:    0.00017 | domain_loss:    0.00174 | bdc_loss:    0.00000\n",
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      "epoch: 12600.00000 | total_loss:    0.00017 | domain_loss:    0.00169 | bdc_loss:    0.00000\n",
      "epoch: 12800.00000 | total_loss:    0.00017 | domain_loss:    0.00168 | bdc_loss:    0.00000\n",
      "epoch: 13000.00000 | total_loss:    0.00018 | domain_loss:    0.00183 | bdc_loss:    0.00000\n",
      "epoch: 13200.00000 | total_loss:    0.00016 | domain_loss:    0.00162 | bdc_loss:    0.00000\n",
      "epoch: 13400.00000 | total_loss:    0.00003 | domain_loss:    0.00029 | bdc_loss:    0.00000\n",
      "epoch: 13600.00000 | total_loss:    0.00003 | domain_loss:    0.00028 | bdc_loss:    0.00000\n",
      "epoch: 13800.00000 | total_loss:    0.00003 | domain_loss:    0.00026 | bdc_loss:    0.00000\n",
      "epoch: 14000.00000 | total_loss:    0.00003 | domain_loss:    0.00033 | bdc_loss:    0.00000\n",
      "epoch: 14200.00000 | total_loss:    0.00003 | domain_loss:    0.00025 | bdc_loss:    0.00000\n",
      "epoch: 14400.00000 | total_loss:    0.00002 | domain_loss:    0.00021 | bdc_loss:    0.00000\n",
      "epoch: 14600.00000 | total_loss:    0.00002 | domain_loss:    0.00019 | bdc_loss:    0.00000\n",
      "epoch: 14800.00000 | total_loss:    0.00002 | domain_loss:    0.00017 | bdc_loss:    0.00000\n",
      "epoch: 15000.00000 | total_loss:    0.00002 | domain_loss:    0.00023 | bdc_loss:    0.00000\n",
      "epoch: 15200.00000 | total_loss:    0.00002 | domain_loss:    0.00025 | bdc_loss:    0.00000\n",
      "epoch: 15400.00000 | total_loss:    0.00004 | domain_loss:    0.00041 | bdc_loss:    0.00000\n",
      "epoch: 15600.00000 | total_loss:    0.00002 | domain_loss:    0.00019 | bdc_loss:    0.00000\n",
      "epoch: 15800.00000 | total_loss:    0.00002 | domain_loss:    0.00020 | bdc_loss:    0.00000\n",
      "epoch: 16000.00000 | total_loss:    0.00002 | domain_loss:    0.00018 | bdc_loss:    0.00000\n",
      "epoch: 16200.00000 | total_loss:    0.00001 | domain_loss:    0.00014 | bdc_loss:    0.00000\n",
      "epoch: 16400.00000 | total_loss:    0.00001 | domain_loss:    0.00014 | bdc_loss:    0.00000\n",
      "epoch: 16600.00000 | total_loss:    0.00002 | domain_loss:    0.00020 | bdc_loss:    0.00000\n",
      "epoch: 16800.00000 | total_loss:    0.00001 | domain_loss:    0.00013 | bdc_loss:    0.00000\n",
      "epoch: 17000.00000 | total_loss:    0.00001 | domain_loss:    0.00014 | bdc_loss:    0.00000\n",
      "epoch: 17200.00000 | total_loss:    0.00002 | domain_loss:    0.00020 | bdc_loss:    0.00000\n",
      "epoch: 17400.00000 | total_loss:    0.00001 | domain_loss:    0.00013 | bdc_loss:    0.00000\n",
      "epoch: 17600.00000 | total_loss:    0.00003 | domain_loss:    0.00032 | bdc_loss:    0.00000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 17800.00000 | total_loss:    0.00001 | domain_loss:    0.00013 | bdc_loss:    0.00000\n",
      "epoch: 18000.00000 | total_loss:    0.00001 | domain_loss:    0.00013 | bdc_loss:    0.00000\n",
      "epoch: 18200.00000 | total_loss:    0.00001 | domain_loss:    0.00013 | bdc_loss:    0.00000\n",
      "epoch: 18400.00000 | total_loss:    0.00001 | domain_loss:    0.00013 | bdc_loss:    0.00000\n",
      "epoch: 18600.00000 | total_loss:    0.00001 | domain_loss:    0.00013 | bdc_loss:    0.00000\n",
      "epoch: 18800.00000 | total_loss:    0.00001 | domain_loss:    0.00012 | bdc_loss:    0.00000\n",
      "epoch: 19000.00000 | total_loss:    0.00001 | domain_loss:    0.00013 | bdc_loss:    0.00000\n",
      "epoch: 19200.00000 | total_loss:    0.00001 | domain_loss:    0.00011 | bdc_loss:    0.00000\n",
      "epoch: 19400.00000 | total_loss:    0.00002 | domain_loss:    0.00018 | bdc_loss:    0.00000\n",
      "epoch: 19600.00000 | total_loss:    0.00002 | domain_loss:    0.00020 | bdc_loss:    0.00000\n",
      "epoch: 19800.00000 | total_loss:    0.00002 | domain_loss:    0.00016 | bdc_loss:    0.00000\n"
     ]
    }
   ],
   "source": [
    "epoch_number_list = []\n",
    "total_loss_list = []\n",
    "domain_loss_list = []\n",
    "boundary_loss_list = []\n",
    "ux_list = []\n",
    "dudx_list = []\n",
    "d2udx2_list = []\n",
    "pde_model_train.load_weights('picn_initial_weights.h5')\n",
    "pde_model_train.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001), loss=\"mse\", loss_weights = [0.1, 0.9])\n",
    "pde_model_train.fit(x=training_input_data, \n",
    "                    y=training_label_data, \n",
    "                    epochs=20000, verbose=0,\n",
    "                    callbacks=callbacks)\n",
    "pde_model_train.load_weights('picn_best_weights.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "983f2f78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=pd.DataFrame({'epoch': epoch_number_list, \n",
    "                 'total_loss': total_loss_list, \n",
    "                 'governing_loss': domain_loss_list, \n",
    "                 'boundary_loss': boundary_loss_list})\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.plot( 'epoch', 'total_loss', data=df, marker='o', markerfacecolor='red', markersize=2, color='skyblue', linewidth=3)\n",
    "plt.plot( 'epoch', 'governing_loss', data=df, marker='', color='green', linewidth=2, linestyle='dashed')\n",
    "plt.plot( 'epoch', 'boundary_loss', data=df, marker='', color='blue', linewidth=2, linestyle='dashed')\n",
    "plt.legend()\n",
    "#plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7e0a3249",
   "metadata": {},
   "outputs": [],
   "source": [
    "[_, u_pred, dudx_pred, d2udx2_pred] = pde_model.predict(training_input_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "984e8c2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 4))\n",
    "plt.plot(x_domain,u_pred[0,:,0],label='u_pred')\n",
    "plt.plot(x_domain,U(x_domain),label='truth')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('u_pred')\n",
    "plt.title('u-prediction')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.plot(x_domain,U(x_domain),label='truth')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('dudx_pred')\n",
    "plt.title('dudx-prediction')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.plot(x_domain,u_pred[0,:,0]-U(x_domain),label='error')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('d2udx2_pred')\n",
    "plt.title('d2udx2-prediction')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8466b730",
   "metadata": {},
   "source": [
    "# 7. Save the Process Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "262c56f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "picn_process_data = {'x_domain':x_domain,\n",
    "                     'epoch_number_list':np.asarray(epoch_number_list),\n",
    "                     'total_loss_list':np.asarray(total_loss_list),\n",
    "                     'domain_loss_list':np.asarray(domain_loss_list),\n",
    "                     'boundary_loss_list':np.asarray(boundary_loss_list),\n",
    "                     'ux_list':np.vstack(ux_list),\n",
    "                     'dudx_list':np.vstack(dudx_list),\n",
    "                     'd2udx2_list':np.vstack(d2udx2_list)}\n",
    "savemat('picn_process_data.mat', picn_process_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8958426",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
